Overview

Brought to you by YData

Dataset statistics

Number of variables25
Number of observations144175
Missing cells1
Missing cells (%)< 0.1%
Duplicate rows1
Duplicate rows (%)< 0.1%
Total size in memory27.5 MiB
Average record size in memory200.0 B

Variable types

Text2
Categorical3
Numeric20

Alerts

Dataset has 1 (< 0.1%) duplicate rowsDuplicates
Deviation of star ratings is highly overall correlated with Rating and 2 other fieldsHigh correlation
FOG Index is highly overall correlated with Flesch Reading Ease and 1 other fieldsHigh correlation
Flesch Reading Ease is highly overall correlated with FOG IndexHigh correlation
Rating is highly overall correlated with Deviation of star ratingsHigh correlation
Topic_3 is highly overall correlated with breadth and 1 other fieldsHigh correlation
avg_rating is highly overall correlated with Deviation of star ratings and 2 other fieldsHigh correlation
breadth is highly overall correlated with Topic_3High correlation
depth is highly overall correlated with Topic_3High correlation
num_of_enrolled is highly overall correlated with num_of_ratings and 2 other fieldsHigh correlation
num_of_ratings is highly overall correlated with Deviation of star ratings and 4 other fieldsHigh correlation
num_of_reviews is highly overall correlated with avg_rating and 3 other fieldsHigh correlation
num_of_top_instructor_courses is highly overall correlated with num_of_top_instructor_leanersHigh correlation
num_of_top_instructor_leaners is highly overall correlated with num_of_enrolled and 3 other fieldsHigh correlation
sentiment_score_continuous is highly overall correlated with sentiment_score_discrete and 1 other fieldsHigh correlation
sentiment_score_discrete is highly overall correlated with sentiment_score_continuousHigh correlation
sst5_sentiment_score is highly overall correlated with sentiment_score_continuousHigh correlation
text_length is highly overall correlated with FOG IndexHigh correlation
Rating is highly imbalanced (62.4%) Imbalance
helpfulness is highly skewed (γ1 = 45.21948824) Skewed
helpfulness has 138507 (96.1%) zeros Zeros

Reproduction

Analysis started2025-01-16 04:37:26.604102
Analysis finished2025-01-16 04:39:05.069000
Duration1 minute and 38.46 seconds
Software versionydata-profiling vv4.12.1
Download configurationconfig.json

Variables

Distinct205
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
2025-01-16T13:39:05.493918image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Length

Max length79
Median length56
Mean length21.42463
Min length2

Characters and Unicode

Total characters3088896
Distinct characters30
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowfoundations-of-cybersecurity
2nd rowfoundations-of-cybersecurity
3rd rowfoundations-of-cybersecurity
4th rowfoundations-of-cybersecurity
5th rowfoundations-of-cybersecurity
ValueCountFrequency (%)
python-data 9731
 
6.7%
foundations-user-experience-design 9550
 
6.6%
python 9511
 
6.6%
python-network-data 6899
 
4.8%
html 6410
 
4.4%
foundations-of-cybersecurity 5252
 
3.6%
html-css-javascript-for-web-developers 4000
 
2.8%
matlab 3860
 
2.7%
introduction-tensorflow 3683
 
2.6%
python-basics 3658
 
2.5%
Other values (195) 81621
56.6%
2025-01-16T13:39:06.378252image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 285306
 
9.2%
e 262159
 
8.5%
t 260689
 
8.4%
o 243376
 
7.9%
n 231964
 
7.5%
a 207012
 
6.7%
r 197543
 
6.4%
i 193689
 
6.3%
s 173482
 
5.6%
d 124276
 
4.0%
Other values (20) 909400
29.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3088896
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
- 285306
 
9.2%
e 262159
 
8.5%
t 260689
 
8.4%
o 243376
 
7.9%
n 231964
 
7.5%
a 207012
 
6.7%
r 197543
 
6.4%
i 193689
 
6.3%
s 173482
 
5.6%
d 124276
 
4.0%
Other values (20) 909400
29.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3088896
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
- 285306
 
9.2%
e 262159
 
8.5%
t 260689
 
8.4%
o 243376
 
7.9%
n 231964
 
7.5%
a 207012
 
6.7%
r 197543
 
6.4%
i 193689
 
6.3%
s 173482
 
5.6%
d 124276
 
4.0%
Other values (20) 909400
29.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3088896
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
- 285306
 
9.2%
e 262159
 
8.5%
t 260689
 
8.4%
o 243376
 
7.9%
n 231964
 
7.5%
a 207012
 
6.7%
r 197543
 
6.4%
i 193689
 
6.3%
s 173482
 
5.6%
d 124276
 
4.0%
Other values (20) 909400
29.4%

Rating
Categorical

High correlation  Imbalance 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
5
119428 
4
17745 
3
 
4420
2
 
1410
1
 
1172

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters144175
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5
2nd row5
3rd row5
4th row5
5th row5

Common Values

ValueCountFrequency (%)
5 119428
82.8%
4 17745
 
12.3%
3 4420
 
3.1%
2 1410
 
1.0%
1 1172
 
0.8%

Length

2025-01-16T13:39:06.585571image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-16T13:39:06.772807image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
5 119428
82.8%
4 17745
 
12.3%
3 4420
 
3.1%
2 1410
 
1.0%
1 1172
 
0.8%

Most occurring characters

ValueCountFrequency (%)
5 119428
82.8%
4 17745
 
12.3%
3 4420
 
3.1%
2 1410
 
1.0%
1 1172
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 144175
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
5 119428
82.8%
4 17745
 
12.3%
3 4420
 
3.1%
2 1410
 
1.0%
1 1172
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 144175
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
5 119428
82.8%
4 17745
 
12.3%
3 4420
 
3.1%
2 1410
 
1.0%
1 1172
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 144175
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
5 119428
82.8%
4 17745
 
12.3%
3 4420
 
3.1%
2 1410
 
1.0%
1 1172
 
0.8%

avg_rating
Real number (ℝ)

High correlation 

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.7422285
Minimum3.8
Maximum4.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2025-01-16T13:39:06.941032image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum3.8
5-th percentile4.6
Q14.7
median4.8
Q34.8
95-th percentile4.9
Maximum4.9
Range1.1
Interquartile range (IQR)0.1

Descriptive statistics

Standard deviation0.10456217
Coefficient of variation (CV)0.022049163
Kurtosis4.129883
Mean4.7422285
Median Absolute Deviation (MAD)0.1
Skewness-1.3300321
Sum683710.8
Variance0.010933247
MonotonicityNot monotonic
2025-01-16T13:39:07.107717image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
4.8 68040
47.2%
4.7 39189
27.2%
4.6 18048
 
12.5%
4.9 13032
 
9.0%
4.5 3679
 
2.6%
4.4 1405
 
1.0%
4.3 604
 
0.4%
4.1 64
 
< 0.1%
3.9 40
 
< 0.1%
4 39
 
< 0.1%
Other values (2) 35
 
< 0.1%
ValueCountFrequency (%)
3.8 8
 
< 0.1%
3.9 40
 
< 0.1%
4 39
 
< 0.1%
4.1 64
 
< 0.1%
4.2 27
 
< 0.1%
4.3 604
 
0.4%
4.4 1405
 
1.0%
4.5 3679
 
2.6%
4.6 18048
12.5%
4.7 39189
27.2%
ValueCountFrequency (%)
4.9 13032
 
9.0%
4.8 68040
47.2%
4.7 39189
27.2%
4.6 18048
 
12.5%
4.5 3679
 
2.6%
4.4 1405
 
1.0%
4.3 604
 
0.4%
4.2 27
 
< 0.1%
4.1 64
 
< 0.1%
4 39
 
< 0.1%

num_of_ratings
Real number (ℝ)

High correlation 

Distinct193
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35941.503
Minimum13
Maximum229752
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2025-01-16T13:39:07.314173image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum13
5-th percentile1170
Q13661
median13222
Q327905
95-th percentile229752
Maximum229752
Range229739
Interquartile range (IQR)24244

Descriptive statistics

Standard deviation57947.916
Coefficient of variation (CV)1.6122842
Kurtosis5.3873647
Mean35941.503
Median Absolute Deviation (MAD)10238
Skewness2.4697645
Sum5.1818663 × 109
Variance3.357961 × 109
MonotonicityNot monotonic
2025-01-16T13:39:07.522502image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
96077 9731
 
6.7%
69498 9550
 
6.6%
229752 9511
 
6.6%
44178 6899
 
4.8%
27564 6410
 
4.4%
27905 5252
 
3.6%
16672 4000
 
2.8%
17700 3860
 
2.7%
19521 3683
 
2.6%
17773 3658
 
2.5%
Other values (183) 81621
56.6%
ValueCountFrequency (%)
13 1
 
< 0.1%
19 6
 
< 0.1%
24 24
< 0.1%
28 8
 
< 0.1%
30 8
 
< 0.1%
39 18
< 0.1%
41 10
< 0.1%
42 13
< 0.1%
52 11
< 0.1%
55 11
< 0.1%
ValueCountFrequency (%)
229752 9511
6.6%
96077 9731
6.7%
69498 9550
6.6%
44178 6899
4.8%
27905 5252
3.6%
27564 6410
4.4%
21320 2616
 
1.8%
19521 3683
 
2.6%
17773 3658
 
2.5%
17700 3860
 
2.7%

helpfulness
Real number (ℝ)

Skewed  Zeros 

Distinct77
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.15305705
Minimum0
Maximum239
Zeros138507
Zeros (%)96.1%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2025-01-16T13:39:07.735633image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum239
Range239
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.9674644
Coefficient of variation (CV)12.854452
Kurtosis3444.0959
Mean0.15305705
Median Absolute Deviation (MAD)0
Skewness45.219488
Sum22067
Variance3.8709163
MonotonicityNot monotonic
2025-01-16T13:39:07.944622image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 138507
96.1%
1 3188
 
2.2%
2 780
 
0.5%
3 378
 
0.3%
4 272
 
0.2%
5 179
 
0.1%
6 133
 
0.1%
7 94
 
0.1%
8 88
 
0.1%
9 82
 
0.1%
Other values (67) 474
 
0.3%
ValueCountFrequency (%)
0 138507
96.1%
1 3188
 
2.2%
2 780
 
0.5%
3 378
 
0.3%
4 272
 
0.2%
5 179
 
0.1%
6 133
 
0.1%
7 94
 
0.1%
8 88
 
0.1%
9 82
 
0.1%
ValueCountFrequency (%)
239 1
< 0.1%
202 1
< 0.1%
144 1
< 0.1%
138 1
< 0.1%
136 1
< 0.1%
118 1
< 0.1%
114 1
< 0.1%
111 1
< 0.1%
99 1
< 0.1%
96 1
< 0.1%
Distinct118705
Distinct (%)82.3%
Missing1
Missing (%)< 0.1%
Memory size1.1 MiB
2025-01-16T13:39:08.774719image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Length

Max length5592
Median length2013
Mean length108.38946
Min length1

Characters and Unicode

Total characters15626942
Distinct characters66
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique115781 ?
Unique (%)80.3%

Sample

1st rowThe course is well paced and they get you comfortable with the topics even though we do not have any sort of prior exposure in this field It is very good for the beginners who are new to this field
2nd rowInformation was well organized, easy to learn, and study with frequent note writing, and some breaks You can learn a good brief summary of whats to come, and what to research more in the future
3rd rowFor a foundation course, this one was easy to understand, it explained all basic concepts in a fluid way and built up the base for the upcoming courses Im eager to move on to the other courses now
4th rowI think this is a great start for anyone who is starting from absolute zero I think that since Ive been toying with the idea of getting into Cybersecurity for 2 years now, it was a great refresher
5th rowSurprised by the quality of this course repeating items so you learn by seeing definitions and concepts over and over again while using great analogy to make difficult concept understandable
ValueCountFrequency (%)
the 124608
 
4.6%
course 95878
 
3.5%
and 91495
 
3.4%
to 90952
 
3.4%
a 64359
 
2.4%
i 63171
 
2.3%
of 49915
 
1.8%
this 47640
 
1.8%
is 44561
 
1.6%
for 42052
 
1.6%
Other values (43809) 1987458
73.6%
2025-01-16T13:39:09.635443image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2580075
16.5%
e 1585200
 
10.1%
t 1071287
 
6.9%
o 1034247
 
6.6%
a 931011
 
6.0%
n 893004
 
5.7%
r 853031
 
5.5%
s 837422
 
5.4%
i 812315
 
5.2%
l 547152
 
3.5%
Other values (56) 4482198
28.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 15626942
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2580075
16.5%
e 1585200
 
10.1%
t 1071287
 
6.9%
o 1034247
 
6.6%
a 931011
 
6.0%
n 893004
 
5.7%
r 853031
 
5.5%
s 837422
 
5.4%
i 812315
 
5.2%
l 547152
 
3.5%
Other values (56) 4482198
28.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 15626942
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2580075
16.5%
e 1585200
 
10.1%
t 1071287
 
6.9%
o 1034247
 
6.6%
a 931011
 
6.0%
n 893004
 
5.7%
r 853031
 
5.5%
s 837422
 
5.4%
i 812315
 
5.2%
l 547152
 
3.5%
Other values (56) 4482198
28.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 15626942
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2580075
16.5%
e 1585200
 
10.1%
t 1071287
 
6.9%
o 1034247
 
6.6%
a 931011
 
6.0%
n 893004
 
5.7%
r 853031
 
5.5%
s 837422
 
5.4%
i 812315
 
5.2%
l 547152
 
3.5%
Other values (56) 4482198
28.7%

num_of_reviews
Real number (ℝ)

High correlation 

Distinct178
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3914.6249
Minimum1
Maximum10000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2025-01-16T13:39:10.096184image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile212
Q1836
median2698
Q36623
95-th percentile10000
Maximum10000
Range9999
Interquartile range (IQR)5787

Descriptive statistics

Standard deviation3599.9527
Coefficient of variation (CV)0.91961627
Kurtosis-1.0379363
Mean3914.6249
Median Absolute Deviation (MAD)2112
Skewness0.70480703
Sum5.6439104 × 108
Variance12959660
MonotonicityNot monotonic
2025-01-16T13:39:10.326139image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10000 28792
 
20.0%
7113 6899
 
4.8%
6623 6410
 
4.4%
5510 5252
 
3.6%
4131 4000
 
2.8%
4032 3860
 
2.7%
3809 3683
 
2.6%
3784 3658
 
2.5%
2854 2771
 
1.9%
2779 2711
 
1.9%
Other values (168) 76139
52.8%
ValueCountFrequency (%)
1 1
 
< 0.1%
2 2
 
< 0.1%
5 5
 
< 0.1%
6 6
 
< 0.1%
7 6
 
< 0.1%
8 40
< 0.1%
9 27
< 0.1%
10 29
< 0.1%
11 22
< 0.1%
12 11
 
< 0.1%
ValueCountFrequency (%)
10000 28792
20.0%
7113 6899
 
4.8%
6623 6410
 
4.4%
5510 5252
 
3.6%
4131 4000
 
2.8%
4032 3860
 
2.7%
3809 3683
 
2.6%
3784 3658
 
2.5%
2854 2771
 
1.9%
2779 2711
 
1.9%

num_of_enrolled
Real number (ℝ)

High correlation 

Distinct205
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean673416.31
Minimum1507
Maximum3205753
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2025-01-16T13:39:10.551918image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum1507
5-th percentile65412
Q1182631
median408135
Q3988223
95-th percentile3205753
Maximum3205753
Range3204246
Interquartile range (IQR)805592

Descriptive statistics

Standard deviation773910.41
Coefficient of variation (CV)1.1492303
Kurtosis4.7074391
Mean673416.31
Median Absolute Deviation (MAD)261381
Skewness2.2424477
Sum9.7089796 × 1010
Variance5.9893732 × 1011
MonotonicityNot monotonic
2025-01-16T13:39:10.767682image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1076350 9731
 
6.7%
1361423 9550
 
6.6%
3205753 9511
 
6.6%
682235 6899
 
4.8%
574559 6410
 
4.4%
988223 5252
 
3.6%
1171384 4000
 
2.8%
497579 3860
 
2.7%
385916 3683
 
2.6%
459997 3658
 
2.5%
Other values (195) 81621
56.6%
ValueCountFrequency (%)
1507 8
< 0.1%
2165 11
< 0.1%
2233 6
< 0.1%
2240 13
< 0.1%
2429 6
< 0.1%
3736 8
< 0.1%
3802 1
 
< 0.1%
4033 12
< 0.1%
5198 11
< 0.1%
5932 9
< 0.1%
ValueCountFrequency (%)
3205753 9511
6.6%
1361423 9550
6.6%
1171384 4000
2.8%
1076350 9731
6.7%
988223 5252
3.6%
682235 6899
4.8%
582205 2626
 
1.8%
574559 6410
4.4%
540079 2597
 
1.8%
535924 2213
 
1.5%

num_of_top_instructor_courses
Real number (ℝ)

High correlation 

Distinct29
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean112.02124
Minimum2
Maximum1675
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2025-01-16T13:39:10.964333image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile4
Q19
median60
Q3129
95-th percentile325
Maximum1675
Range1673
Interquartile range (IQR)120

Descriptive statistics

Standard deviation216.49506
Coefficient of variation (CV)1.9326251
Kurtosis33.846437
Mean112.02124
Median Absolute Deviation (MAD)51
Skewness5.2003808
Sum16150662
Variance46870.111
MonotonicityNot monotonic
2025-01-16T13:39:11.159641image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
60 38906
27.0%
325 26626
18.5%
9 11394
 
7.9%
4 8952
 
6.2%
129 7919
 
5.5%
5 6469
 
4.5%
18 6358
 
4.4%
8 4940
 
3.4%
2 4867
 
3.4%
12 4743
 
3.3%
Other values (19) 23001
16.0%
ValueCountFrequency (%)
2 4867
3.4%
3 818
 
0.6%
4 8952
6.2%
5 6469
4.5%
6 1546
 
1.1%
7 2866
 
2.0%
8 4940
3.4%
9 11394
7.9%
10 50
 
< 0.1%
12 4743
3.3%
ValueCountFrequency (%)
1675 1945
 
1.3%
325 26626
18.5%
196 90
 
0.1%
129 7919
 
5.5%
87 171
 
0.1%
66 19
 
< 0.1%
60 38906
27.0%
58 2431
 
1.7%
53 1451
 
1.0%
48 11
 
< 0.1%

num_of_top_instructor_leaners
Real number (ℝ)

High correlation 

Distinct83
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3625516.4
Minimum3945
Maximum11153139
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2025-01-16T13:39:11.378724image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum3945
5-th percentile167950
Q1517137
median1245385
Q34384601
95-th percentile11153139
Maximum11153139
Range11149194
Interquartile range (IQR)3867464

Descriptive statistics

Standard deviation3931982.4
Coefficient of variation (CV)1.0845303
Kurtosis-0.32362373
Mean3625516.4
Median Absolute Deviation (MAD)1123168
Skewness1.0520913
Sum5.2270883 × 1011
Variance1.5460486 × 1013
MonotonicityNot monotonic
2025-01-16T13:39:11.564018image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4384601 38906
27.0%
11153139 26626
18.5%
954353 7919
 
5.5%
1071441 6119
 
4.2%
517137 4436
 
3.1%
596976 4414
 
3.1%
1245385 4361
 
3.0%
506004 3972
 
2.8%
426823 3816
 
2.6%
528545 3765
 
2.6%
Other values (73) 39841
27.6%
ValueCountFrequency (%)
3945 8
 
< 0.1%
6855 6
 
< 0.1%
7841 22
< 0.1%
8506 39
< 0.1%
8682 8
 
< 0.1%
8703 10
 
< 0.1%
18193 23
< 0.1%
19837 18
< 0.1%
21526 41
< 0.1%
29959 44
< 0.1%
ValueCountFrequency (%)
11153139 26626
18.5%
4384601 38906
27.0%
3081791 1451
 
1.0%
2767486 1945
 
1.3%
1245385 4361
 
3.0%
1071441 6119
 
4.2%
1065753 90
 
0.1%
1014757 2431
 
1.7%
954353 7919
 
5.5%
892947 1
 
< 0.1%

depth
Real number (ℝ)

High correlation 

Distinct102398
Distinct (%)71.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-56.191015
Minimum-100
Maximum-8.7987732
Zeros0
Zeros (%)0.0%
Negative144175
Negative (%)100.0%
Memory size1.1 MiB
2025-01-16T13:39:11.785385image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum-100
5-th percentile-82.665539
Q1-81.712639
median-63.77041
Q3-31.270756
95-th percentile-13.037956
Maximum-8.7987732
Range91.201227
Interquartile range (IQR)50.441884

Descriptive statistics

Standard deviation27.01916
Coefficient of variation (CV)-0.48084485
Kurtosis-1.2908057
Mean-56.191015
Median Absolute Deviation (MAD)18.151802
Skewness0.4486042
Sum-8101339.5
Variance730.03502
MonotonicityNot monotonic
2025-01-16T13:39:11.994109image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-81.02716281 5217
 
3.6%
-100 2653
 
1.8%
-13.03795572 2095
 
1.5%
-11.50166211 1670
 
1.2%
-80.93541039 1537
 
1.1%
-80.89876638 1365
 
0.9%
-80.86473527 1309
 
0.9%
-12.66063664 937
 
0.6%
-82.00227236 734
 
0.5%
-12.67459405 726
 
0.5%
Other values (102388) 125932
87.3%
ValueCountFrequency (%)
-100 2653
1.8%
-85.0804667 1
 
< 0.1%
-85.05122223 1
 
< 0.1%
-85.04970454 4
 
< 0.1%
-84.99239666 1
 
< 0.1%
-84.94088167 11
 
< 0.1%
-84.93204365 5
 
< 0.1%
-84.842206 6
 
< 0.1%
-84.83006538 5
 
< 0.1%
-84.76259265 1
 
< 0.1%
ValueCountFrequency (%)
-8.798773191 1
< 0.1%
-8.839094015 1
< 0.1%
-9.126066095 1
< 0.1%
-9.415730643 1
< 0.1%
-9.534633016 1
< 0.1%
-9.581133571 1
< 0.1%
-9.650485166 1
< 0.1%
-9.706895053 1
< 0.1%
-9.77316162 1
< 0.1%
-9.778012839 1
< 0.1%

breadth
Real number (ℝ)

High correlation 

Distinct69620
Distinct (%)48.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1910836
Minimum0.031277315
Maximum4.4330168
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2025-01-16T13:39:12.209407image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0.031277315
5-th percentile0.37814931
Q10.6680753
median0.69947136
Q31.4939761
95-th percentile2.8867949
Maximum4.4330168
Range4.4017395
Interquartile range (IQR)0.82590081

Descriptive statistics

Standard deviation0.92778873
Coefficient of variation (CV)0.77894511
Kurtosis1.6923556
Mean1.1910836
Median Absolute Deviation (MAD)0.18636348
Skewness1.5717753
Sum171724.48
Variance0.86079194
MonotonicityNot monotonic
2025-01-16T13:39:12.422209image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.699471365 43002
29.8%
2.840657022 5549
 
3.8%
0.647271721 2653
 
1.8%
2.886794893 2293
 
1.6%
1.493976116 2095
 
1.5%
1.079430014 1670
 
1.2%
3.98605896 1611
 
1.1%
4.433016776 1469
 
1.0%
2.563043433 937
 
0.6%
3.053655198 726
 
0.5%
Other values (69610) 82170
57.0%
ValueCountFrequency (%)
0.031277315 1
< 0.1%
0.036646499 1
< 0.1%
0.037259207 1
< 0.1%
0.03727452 1
< 0.1%
0.039460533 1
< 0.1%
0.047056842 1
< 0.1%
0.048498549 1
< 0.1%
0.050129461 1
< 0.1%
0.05067275 1
< 0.1%
0.051788469 1
< 0.1%
ValueCountFrequency (%)
4.433016776 1469
1.0%
4.432271889 1
 
< 0.1%
4.432031292 2
 
< 0.1%
4.429161311 1
 
< 0.1%
4.42762739 1
 
< 0.1%
4.427098982 1
 
< 0.1%
4.426473879 3
 
< 0.1%
4.425900817 1
 
< 0.1%
4.424742466 3
 
< 0.1%
4.424676444 1
 
< 0.1%

Topic_1
Real number (ℝ)

Distinct51522
Distinct (%)35.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.14097669
Minimum5.33 × 10-20
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2025-01-16T13:39:12.654115image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum5.33 × 10-20
5-th percentile1.52 × 10-19
Q15.67 × 10-19
median1.48 × 10-16
Q30.026065148
95-th percentile0.92091331
Maximum1
Range1
Interquartile range (IQR)0.026065148

Descriptive statistics

Standard deviation0.28871585
Coefficient of variation (CV)2.0479687
Kurtosis2.403007
Mean0.14097669
Median Absolute Deviation (MAD)1.479268 × 10-16
Skewness1.9537861
Sum20325.315
Variance0.083356845
MonotonicityNot monotonic
2025-01-16T13:39:12.886011image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 5549
 
3.8%
0.2 2653
 
1.8%
0.000686351 2095
 
1.5%
0.657179495 1670
 
1.2%
8.62 × 10-201537
 
1.1%
7.92 × 10-201365
 
0.9%
7.32 × 10-201309
 
0.9%
0.000910666 938
 
0.7%
1.01 × 10-18912
 
0.6%
0.00072562 726
 
0.5%
Other values (51512) 125421
87.0%
ValueCountFrequency (%)
5.33 × 10-205
< 0.1%
5.4 × 10-201
 
< 0.1%
5.56 × 10-208
< 0.1%
5.66 × 10-203
 
< 0.1%
5.91 × 10-201
 
< 0.1%
6.44 × 10-201
 
< 0.1%
6.66 × 10-201
 
< 0.1%
6.68 × 10-201
 
< 0.1%
6.9 × 10-201
 
< 0.1%
7.04 × 10-201
 
< 0.1%
ValueCountFrequency (%)
1 5549
3.8%
0.999963129 1
 
< 0.1%
0.999908808 2
 
< 0.1%
0.999869759 1
 
< 0.1%
0.999754134 2
 
< 0.1%
0.999749561 1
 
< 0.1%
0.999629503 1
 
< 0.1%
0.99958591 11
 
< 0.1%
0.999580533 7
 
< 0.1%
0.999518341 1
 
< 0.1%

Topic_2
Real number (ℝ)

Distinct48058
Distinct (%)33.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1369888
Minimum5.4 × 10-20
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2025-01-16T13:39:13.096181image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum5.4 × 10-20
5-th percentile1.06 × 10-19
Q15.19 × 10-19
median4.54 × 10-18
Q30.041602561
95-th percentile0.80390715
Maximum1
Range1
Interquartile range (IQR)0.041602561

Descriptive statistics

Standard deviation0.2752311
Coefficient of variation (CV)2.0091504
Kurtosis2.2878236
Mean0.1369888
Median Absolute Deviation (MAD)4.4608 × 10-18
Skewness1.9283522
Sum19750.36
Variance0.075752161
MonotonicityNot monotonic
2025-01-16T13:39:13.310341image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.06 × 10-195243
 
3.6%
0.2 2653
 
1.8%
1 2293
 
1.6%
0.750730944 2095
 
1.5%
0.013330328 1670
 
1.2%
7.92 × 10-201365
 
0.9%
7.32 × 10-201309
 
0.9%
0.007786906 937
 
0.6%
1.01 × 10-18925
 
0.6%
0.006227935 726
 
0.5%
Other values (48048) 124959
86.7%
ValueCountFrequency (%)
5.4 × 10-201
 
< 0.1%
6.05 × 10-2011
< 0.1%
6.37 × 10-208
< 0.1%
6.58 × 10-201
 
< 0.1%
6.68 × 10-201
 
< 0.1%
6.76 × 10-201
 
< 0.1%
6.94 × 10-201
 
< 0.1%
7.04 × 10-201
 
< 0.1%
7.06 × 10-201
 
< 0.1%
7.11 × 10-201
 
< 0.1%
ValueCountFrequency (%)
1 2293
1.6%
0.999995717 1
 
< 0.1%
0.999993661 1
 
< 0.1%
0.999870271 4
 
< 0.1%
0.9998577 1
 
< 0.1%
0.999831214 1
 
< 0.1%
0.999745855 1
 
< 0.1%
0.999696796 1
 
< 0.1%
0.999673719 1
 
< 0.1%
0.999671248 1
 
< 0.1%

Topic_3
Real number (ℝ)

High correlation 

Distinct69118
Distinct (%)47.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.6139252
Minimum5.4 × 10-20
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2025-01-16T13:39:13.774003image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum5.4 × 10-20
5-th percentile1.06 × 10-19
Q10.24010873
median0.72035444
Q31
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.75989127

Descriptive statistics

Standard deviation0.38922587
Coefficient of variation (CV)0.63399559
Kurtosis-1.5792722
Mean0.6139252
Median Absolute Deviation (MAD)0.27964556
Skewness-0.30513878
Sum88512.666
Variance0.15149678
MonotonicityNot monotonic
2025-01-16T13:39:13.991382image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 43002
29.8%
1.06 × 10-195224
 
3.6%
0.2 2653
 
1.8%
0.240108726 2095
 
1.5%
0.316035382 1670
 
1.2%
8.62 × 10-201537
 
1.1%
7.92 × 10-201366
 
0.9%
7.32 × 10-201309
 
0.9%
0.187166114 937
 
0.6%
0.166214455 726
 
0.5%
Other values (69108) 83656
58.0%
ValueCountFrequency (%)
5.4 × 10-201
 
< 0.1%
5.66 × 10-203
 
< 0.1%
5.91 × 10-201
 
< 0.1%
6.05 × 10-2011
 
< 0.1%
6.37 × 10-208
 
< 0.1%
6.76 × 10-208
 
< 0.1%
6.94 × 10-201
 
< 0.1%
7.11 × 10-201
 
< 0.1%
7.32 × 10-201309
0.9%
7.38 × 10-201
 
< 0.1%
ValueCountFrequency (%)
1 43002
29.8%
0.999999907 1
 
< 0.1%
0.999999543 1
 
< 0.1%
0.999998474 1
 
< 0.1%
0.99999806 1
 
< 0.1%
0.999995878 1
 
< 0.1%
0.999994324 1
 
< 0.1%
0.999994032 1
 
< 0.1%
0.999991689 1
 
< 0.1%
0.999991452 1
 
< 0.1%

Topic_4
Real number (ℝ)

Distinct40710
Distinct (%)28.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.062265867
Minimum5.66 × 10-20
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2025-01-16T13:39:14.215928image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum5.66 × 10-20
5-th percentile1.06 × 10-19
Q14.03 × 10-19
median1.58 × 10-18
Q30.007812483
95-th percentile0.63435474
Maximum1
Range1
Interquartile range (IQR)0.007812483

Descriptive statistics

Standard deviation0.1976287
Coefficient of variation (CV)3.1739493
Kurtosis11.531268
Mean0.062265867
Median Absolute Deviation (MAD)1.474 × 10-18
Skewness3.5383841
Sum8977.1814
Variance0.039057105
MonotonicityNot monotonic
2025-01-16T13:39:14.437928image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.06 × 10-195234
 
3.6%
0.2 2653
 
1.8%
0.005376354 2095
 
1.5%
0.007812483 1670
 
1.2%
1 1611
 
1.1%
8.62 × 10-201537
 
1.1%
7.32 × 10-201309
 
0.9%
0.800875843 937
 
0.6%
1.01 × 10-18928
 
0.6%
0.003921388 726
 
0.5%
Other values (40700) 125475
87.0%
ValueCountFrequency (%)
5.66 × 10-203
 
< 0.1%
6.05 × 10-2011
 
< 0.1%
6.58 × 10-201
 
< 0.1%
6.76 × 10-208
 
< 0.1%
7.32 × 10-201309
0.9%
7.35 × 10-201
 
< 0.1%
7.64 × 10-2026
 
< 0.1%
7.68 × 10-201
 
< 0.1%
7.83 × 10-201
 
< 0.1%
7.89 × 10-2011
 
< 0.1%
ValueCountFrequency (%)
1 1611
1.1%
0.999954644 1
 
< 0.1%
0.999942945 1
 
< 0.1%
0.999899199 2
 
< 0.1%
0.99980682 1
 
< 0.1%
0.999700569 1
 
< 0.1%
0.999655922 1
 
< 0.1%
0.99965555 1
 
< 0.1%
0.999655094 1
 
< 0.1%
0.999592949 1
 
< 0.1%

Topic_5
Real number (ℝ)

Distinct37326
Distinct (%)25.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.045843439
Minimum5.91 × 10-20
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2025-01-16T13:39:14.681829image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum5.91 × 10-20
5-th percentile1.06 × 10-19
Q13.76 × 10-19
median1.34 × 10-18
Q30.0044258955
95-th percentile0.2
Maximum1
Range1
Interquartile range (IQR)0.0044258955

Descriptive statistics

Standard deviation0.17448836
Coefficient of variation (CV)3.8061796
Kurtosis18.397659
Mean0.045843439
Median Absolute Deviation (MAD)1.222 × 10-18
Skewness4.3853628
Sum6609.4778
Variance0.030446189
MonotonicityNot monotonic
2025-01-16T13:39:14.903487image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.06 × 10-195244
 
3.6%
0.2 2653
 
1.8%
0.003097624 2095
 
1.5%
0.005642311 1670
 
1.2%
8.62 × 10-201537
 
1.1%
1 1469
 
1.0%
7.92 × 10-201365
 
0.9%
0.00326047 937
 
0.6%
1.01 × 10-18929
 
0.6%
0.822910603 726
 
0.5%
Other values (37316) 125550
87.1%
ValueCountFrequency (%)
5.91 × 10-201
 
< 0.1%
6.37 × 10-208
 
< 0.1%
6.44 × 10-201
 
< 0.1%
6.66 × 10-201
 
< 0.1%
6.76 × 10-208
 
< 0.1%
7.06 × 10-201
 
< 0.1%
7.43 × 10-201
 
< 0.1%
7.69 × 10-202
 
< 0.1%
7.8 × 10-203
 
< 0.1%
7.92 × 10-201365
0.9%
ValueCountFrequency (%)
1 1469
1.0%
0.999954323 1
 
< 0.1%
0.99993788 2
 
< 0.1%
0.99974222 1
 
< 0.1%
0.999644206 1
 
< 0.1%
0.999610428 1
 
< 0.1%
0.999564536 1
 
< 0.1%
0.999536265 3
 
< 0.1%
0.999481632 1
 
< 0.1%
0.999462414 1
 
< 0.1%

sentiment_score_continuous
Real number (ℝ)

High correlation 

Distinct3687
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.3066594
Minimum1.015
Maximum4.989
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2025-01-16T13:39:15.124287image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum1.015
5-th percentile2.839
Q14.11
median4.512
Q34.73
95-th percentile4.881
Maximum4.989
Range3.974
Interquartile range (IQR)0.62

Descriptive statistics

Standard deviation0.64060912
Coefficient of variation (CV)0.1487485
Kurtosis4.349708
Mean4.3066594
Median Absolute Deviation (MAD)0.264
Skewness-2.0195068
Sum620912.62
Variance0.41038004
MonotonicityNot monotonic
2025-01-16T13:39:15.340517image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.103 3456
 
2.4%
4.578 1241
 
0.9%
4.833 1144
 
0.8%
4.682 965
 
0.7%
4.249 959
 
0.7%
4.484 857
 
0.6%
4.069 756
 
0.5%
4.758 663
 
0.5%
4.696 629
 
0.4%
4.783 576
 
0.4%
Other values (3677) 132929
92.2%
ValueCountFrequency (%)
1.015 2
< 0.1%
1.03 1
< 0.1%
1.034 1
< 0.1%
1.036 1
< 0.1%
1.039 1
< 0.1%
1.049 1
< 0.1%
1.052 1
< 0.1%
1.053 1
< 0.1%
1.06 1
< 0.1%
1.061 1
< 0.1%
ValueCountFrequency (%)
4.989 3
< 0.1%
4.988 5
< 0.1%
4.987 2
 
< 0.1%
4.985 2
 
< 0.1%
4.984 1
 
< 0.1%
4.983 1
 
< 0.1%
4.982 3
< 0.1%
4.981 3
< 0.1%
4.98 2
 
< 0.1%
4.979 4
< 0.1%

sentiment_score_discrete
Categorical

High correlation 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
5
91311 
4
38857 
3
 
9102
2
 
3388
1
 
1517

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters144175
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row4
3rd row4
4th row5
5th row5

Common Values

ValueCountFrequency (%)
5 91311
63.3%
4 38857
27.0%
3 9102
 
6.3%
2 3388
 
2.3%
1 1517
 
1.1%

Length

2025-01-16T13:39:15.544670image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-16T13:39:15.744636image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
5 91311
63.3%
4 38857
27.0%
3 9102
 
6.3%
2 3388
 
2.3%
1 1517
 
1.1%

Most occurring characters

ValueCountFrequency (%)
5 91311
63.3%
4 38857
27.0%
3 9102
 
6.3%
2 3388
 
2.3%
1 1517
 
1.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 144175
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
5 91311
63.3%
4 38857
27.0%
3 9102
 
6.3%
2 3388
 
2.3%
1 1517
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 144175
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
5 91311
63.3%
4 38857
27.0%
3 9102
 
6.3%
2 3388
 
2.3%
1 1517
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 144175
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
5 91311
63.3%
4 38857
27.0%
3 9102
 
6.3%
2 3388
 
2.3%
1 1517
 
1.1%

sst5_sentiment_score
Categorical

High correlation 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
4
91848 
5
36889 
3
10444 
2
 
4710
1
 
284

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters144175
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row4
3rd row4
4th row4
5th row4

Common Values

ValueCountFrequency (%)
4 91848
63.7%
5 36889
25.6%
3 10444
 
7.2%
2 4710
 
3.3%
1 284
 
0.2%

Length

2025-01-16T13:39:15.917620image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-16T13:39:16.102063image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
4 91848
63.7%
5 36889
25.6%
3 10444
 
7.2%
2 4710
 
3.3%
1 284
 
0.2%

Most occurring characters

ValueCountFrequency (%)
4 91848
63.7%
5 36889
25.6%
3 10444
 
7.2%
2 4710
 
3.3%
1 284
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 144175
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4 91848
63.7%
5 36889
25.6%
3 10444
 
7.2%
2 4710
 
3.3%
1 284
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 144175
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4 91848
63.7%
5 36889
25.6%
3 10444
 
7.2%
2 4710
 
3.3%
1 284
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 144175
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4 91848
63.7%
5 36889
25.6%
3 10444
 
7.2%
2 4710
 
3.3%
1 284
 
0.2%

text_length
Real number (ℝ)

High correlation 

Distinct356
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.741737
Minimum0
Maximum985
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2025-01-16T13:39:16.289401image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q14
median11
Q324
95-th percentile60
Maximum985
Range985
Interquartile range (IQR)20

Descriptive statistics

Standard deviation25.569154
Coefficient of variation (CV)1.3642894
Kurtosis72.236134
Mean18.741737
Median Absolute Deviation (MAD)8
Skewness5.5492691
Sum2702090
Variance653.78166
MonotonicityNot monotonic
2025-01-16T13:39:16.484177image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 12748
 
8.8%
1 12000
 
8.3%
4 6992
 
4.8%
5 6905
 
4.8%
3 6314
 
4.4%
6 6048
 
4.2%
7 5445
 
3.8%
8 5030
 
3.5%
9 4647
 
3.2%
10 4417
 
3.1%
Other values (346) 73629
51.1%
ValueCountFrequency (%)
0 1
 
< 0.1%
1 12000
8.3%
2 12748
8.8%
3 6314
4.4%
4 6992
4.8%
5 6905
4.8%
6 6048
4.2%
7 5445
3.8%
8 5030
 
3.5%
9 4647
 
3.2%
ValueCountFrequency (%)
985 1
< 0.1%
786 1
< 0.1%
748 1
< 0.1%
728 1
< 0.1%
604 1
< 0.1%
582 1
< 0.1%
544 1
< 0.1%
534 1
< 0.1%
518 1
< 0.1%
512 1
< 0.1%

time_lapsed
Real number (ℝ)

Distinct3381
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1363.3769
Minimum-1
Maximum3397
Zeros20
Zeros (%)< 0.1%
Negative1
Negative (%)< 0.1%
Memory size1.1 MiB
2025-01-16T13:39:16.699920image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile180
Q1753
median1459
Q31685
95-th percentile2870
Maximum3397
Range3398
Interquartile range (IQR)932

Descriptive statistics

Standard deviation767.82278
Coefficient of variation (CV)0.56317719
Kurtosis-0.24908813
Mean1363.3769
Median Absolute Deviation (MAD)505
Skewness0.38584537
Sum1.9656487 × 108
Variance589551.82
MonotonicityNot monotonic
2025-01-16T13:39:16.908499image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1644 286
 
0.2%
1645 278
 
0.2%
1643 272
 
0.2%
1664 270
 
0.2%
1657 267
 
0.2%
1650 267
 
0.2%
1663 265
 
0.2%
1601 265
 
0.2%
1659 265
 
0.2%
1658 261
 
0.2%
Other values (3371) 141479
98.1%
ValueCountFrequency (%)
-1 1
 
< 0.1%
0 20
< 0.1%
1 27
< 0.1%
2 32
< 0.1%
3 25
< 0.1%
4 31
< 0.1%
5 40
< 0.1%
6 36
< 0.1%
7 37
< 0.1%
8 34
< 0.1%
ValueCountFrequency (%)
3397 1
< 0.1%
3395 2
< 0.1%
3392 1
< 0.1%
3391 1
< 0.1%
3390 1
< 0.1%
3384 1
< 0.1%
3383 1
< 0.1%
3382 1
< 0.1%
3379 2
< 0.1%
3376 1
< 0.1%

Deviation of star ratings
Real number (ℝ)

High correlation 

Distinct40
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.39759736
Minimum0
Maximum3.9
Zeros8
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2025-01-16T13:39:17.139446image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.1
Q10.2
median0.2
Q30.4
95-th percentile0.9
Maximum3.9
Range3.9
Interquartile range (IQR)0.2

Descriptive statistics

Standard deviation0.4761856
Coefficient of variation (CV)1.1976578
Kurtosis21.947658
Mean0.39759736
Median Absolute Deviation (MAD)0.1
Skewness4.2872441
Sum57323.6
Variance0.22675273
MonotonicityNot monotonic
2025-01-16T13:39:17.588915image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
0.2 59712
41.4%
0.3 30855
21.4%
0.4 12990
 
9.0%
0.1 12750
 
8.8%
0.7 6663
 
4.6%
0.8 6268
 
4.3%
0.6 4461
 
3.1%
0.5 3173
 
2.2%
1.7 1385
 
1.0%
1.8 1353
 
0.9%
Other values (30) 4565
 
3.2%
ValueCountFrequency (%)
0 8
 
< 0.1%
0.1 12750
 
8.8%
0.2 59712
41.4%
0.3 30855
21.4%
0.4 12990
 
9.0%
0.5 3173
 
2.2%
0.6 4461
 
3.1%
0.7 6663
 
4.6%
0.8 6268
 
4.3%
0.9 254
 
0.2%
ValueCountFrequency (%)
3.9 26
 
< 0.1%
3.8 348
0.2%
3.7 339
0.2%
3.6 268
0.2%
3.5 89
 
0.1%
3.4 51
 
< 0.1%
3.3 34
 
< 0.1%
3.2 2
 
< 0.1%
3.1 6
 
< 0.1%
3 2
 
< 0.1%

FOG Index
Real number (ℝ)

High correlation 

Distinct2262
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.424791
Minimum0
Maximum396.36
Zeros15
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2025-01-16T13:39:17.811667image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.4
Q13.2
median11.33
Q316.73
95-th percentile33.4
Maximum396.36
Range396.36
Interquartile range (IQR)13.53

Descriptive statistics

Standard deviation11.810657
Coefficient of variation (CV)0.9505719
Kurtosis39.166966
Mean12.424791
Median Absolute Deviation (MAD)6.72
Skewness3.5747908
Sum1791344.2
Variance139.49162
MonotonicityNot monotonic
2025-01-16T13:39:18.025811image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.4 9628
 
6.7%
0.8 9369
 
6.5%
1.2 4214
 
2.9%
1.6 3612
 
2.5%
10 3405
 
2.4%
2 3357
 
2.3%
8.04 3260
 
2.3%
20.8 3196
 
2.2%
11.6 3070
 
2.1%
2.4 2672
 
1.9%
Other values (2252) 98392
68.2%
ValueCountFrequency (%)
0 15
 
< 0.1%
0.4 9628
6.7%
0.8 9369
6.5%
1.2 4214
2.9%
1.6 3612
 
2.5%
2 3357
 
2.3%
2.4 2672
 
1.9%
2.8 2252
 
1.6%
3.2 1839
 
1.3%
3.6 1576
 
1.1%
ValueCountFrequency (%)
396.36 1
< 0.1%
320.81 1
< 0.1%
306.04 1
< 0.1%
295.16 1
< 0.1%
247.63 1
< 0.1%
234.66 1
< 0.1%
224.36 1
< 0.1%
216.22 1
< 0.1%
208.67 1
< 0.1%
207.46 1
< 0.1%

Flesch Reading Ease
Real number (ℝ)

High correlation 

Distinct1438
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean59.945715
Minimum-3093.59
Maximum206.84
Zeros0
Zeros (%)0.0%
Negative10076
Negative (%)7.0%
Memory size1.1 MiB
2025-01-16T13:39:18.254683image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum-3093.59
5-th percentile-8.73
Q137.98
median62.34
Q385.69
95-th percentile121.22
Maximum206.84
Range3300.43
Interquartile range (IQR)47.71

Descriptive statistics

Standard deviation45.137746
Coefficient of variation (CV)0.75297702
Kurtosis210.82505
Mean59.945715
Median Absolute Deviation (MAD)24.02
Skewness-5.2209111
Sum8642673.5
Variance2037.4161
MonotonicityNot monotonic
2025-01-16T13:39:18.453264image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
121.22 7558
 
5.2%
120.21 6193
 
4.3%
77.91 2592
 
1.8%
-47.99 2131
 
1.5%
119.19 2052
 
1.4%
35.61 2039
 
1.4%
36.62 1925
 
1.3%
75.88 1641
 
1.1%
-6.7 1551
 
1.1%
93.81 1453
 
1.0%
Other values (1428) 115040
79.8%
ValueCountFrequency (%)
-3093.59 1
 
< 0.1%
-1739.99 1
 
< 0.1%
-1147.79 1
 
< 0.1%
-978.59 4
< 0.1%
-902.93 1
 
< 0.1%
-893.99 1
 
< 0.1%
-809.39 2
< 0.1%
-751.7 1
 
< 0.1%
-724.79 3
< 0.1%
-721.59 1
 
< 0.1%
ValueCountFrequency (%)
206.84 15
 
< 0.1%
121.22 7558
5.2%
120.21 6193
4.3%
119.19 2052
 
1.4%
118.18 1334
 
0.9%
117.16 932
 
0.6%
116.15 612
 
0.4%
115.13 450
 
0.3%
114.12 326
 
0.2%
113.1 203
 
0.1%

Interactions

2025-01-16T13:38:58.804995image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:37:43.993141image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:37:47.888331image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:37:52.139477image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:37:55.746409image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:37:59.557573image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:02.610579image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:06.870397image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:10.760292image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:14.989295image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:18.661972image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:21.998263image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:25.180430image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:29.337129image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:33.434554image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:37.477483image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:42.006564image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:45.916864image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:50.137623image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:54.469642image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:59.011315image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:37:44.182320image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:37:48.077351image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:37:52.354868image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:37:55.895560image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:37:59.756327image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:02.766144image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:07.076009image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:10.948308image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:15.173999image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:18.810657image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:22.157085image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:25.345101image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:29.540560image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:33.636488image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:37.915640image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:42.186634image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:46.129935image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:50.367760image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:54.695100image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:59.197762image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:37:44.409530image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:37:48.271766image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:37:52.550049image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:37:56.045675image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:37:59.936242image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:02.913329image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:07.273676image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:11.138688image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:15.351444image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:18.960896image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:22.304896image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:25.479726image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:29.756857image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:33.834013image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:38.110600image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:42.358613image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:46.341718image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:50.570187image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:54.890902image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:59.391274image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:37:44.617704image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:37:48.472231image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:37:52.762782image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:37:56.223269image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:00.115529image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:03.060796image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:07.495155image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:11.347373image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:15.541978image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:19.120273image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:22.463375image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:25.634120image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:29.948784image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:34.034521image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:38.311633image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:42.572705image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:46.520327image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:50.757104image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:55.094436image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:59.624311image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:37:44.814329image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:37:48.669772image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:37:52.977448image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:37:56.426520image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:00.253532image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:03.220438image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:07.709604image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:11.519952image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:15.731791image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:19.266303image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:22.613027image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:25.826553image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:30.165556image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:34.234280image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:38.525095image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:42.789652image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:46.725195image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:50.960052image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:55.295585image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:59.857602image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:37:45.016619image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:37:48.899730image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:37:53.231827image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:37:56.598200image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:00.395717image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:03.415977image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:07.892076image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:11.724752image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:15.956575image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:19.421482image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:22.773629image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:26.059215image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:30.369377image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:34.450182image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:38.748582image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:42.980014image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:46.927973image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:51.164146image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:55.511896image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:39:00.100318image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:37:45.222948image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:37:49.106258image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:37:53.399893image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:37:56.815979image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:00.557715image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:03.653839image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:08.129485image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:11.928926image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:16.161068image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:19.588802image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:22.935881image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:26.259625image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:30.583134image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:34.649277image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:38.991908image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:43.177058image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:47.138288image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:51.398259image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:55.741536image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:39:00.315997image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:37:45.430537image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:37:49.296854image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:37:53.546153image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:37:57.023578image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:00.706439image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:03.894806image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:08.325929image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:12.122685image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:16.371032image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:19.746908image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:23.103493image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:26.466414image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:30.777641image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:34.846937image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:39.220167image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:43.338360image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:47.358433image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:51.599783image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:55.958679image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:39:00.508996image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:37:45.644192image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:37:49.472667image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:37:53.685260image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:37:57.205624image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:00.844339image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:04.095965image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:08.529861image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:12.307494image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:16.581824image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:19.896625image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:23.244277image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:26.666334image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:30.959262image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:35.068699image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:39.430000image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:43.484931image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:47.568762image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:51.816808image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:56.188406image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:39:00.715427image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:37:45.863294image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:37:49.639824image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:37:53.820049image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:37:57.394427image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:00.974204image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:04.283939image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:08.720418image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:12.499859image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:16.774995image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:20.035291image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:23.393121image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:26.849541image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:31.157784image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:35.259295image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:39.630424image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:43.638175image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:47.759054image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:52.024675image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:56.398256image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:39:00.936289image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:37:46.111745image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:37:49.850647image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:37:54.005254image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:37:57.579316image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:01.121892image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:04.483550image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:08.961801image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:12.699115image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:16.971838image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:20.181613image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:23.537697image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:27.048945image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:31.352970image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:35.468125image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:39.826340image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:43.786258image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:47.973997image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:52.238981image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:56.607197image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:39:01.145092image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:37:46.336531image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:37:50.048242image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:37:54.176843image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:37:57.729312image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:01.260840image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:04.687642image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:09.152947image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:12.887239image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:17.165794image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:20.326770image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:23.686128image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:27.242105image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:31.547982image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:35.663883image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:40.054225image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:43.945472image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:48.198464image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:52.414143image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:56.826501image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:39:01.614311image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:37:46.552333image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:37:50.244740image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:37:54.377005image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:37:57.871029image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:01.401745image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:04.893988image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:09.347805image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:13.080079image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:17.381886image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:20.473026image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:23.836027image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:27.421995image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:31.731136image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:35.858724image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:40.252939image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:44.098351image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:48.410219image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:52.585628image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:57.052526image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:39:01.826912image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:37:46.763510image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:37:50.445463image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:37:54.562877image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:37:58.032146image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:01.548944image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:05.133278image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:09.497165image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:13.255568image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:17.560713image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:20.626831image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:23.995574image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:27.604190image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:31.909870image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:36.037209image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:40.464427image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:44.285702image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:48.612277image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:52.754208image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:57.268524image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:39:02.029957image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:37:46.922217image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:37:50.861994image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:37:54.747277image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:37:58.191705image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:01.694657image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:05.576560image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:09.658590image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:13.693784image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:17.710063image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:20.783261image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:24.151946image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:27.814749image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:32.109134image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:36.245587image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:40.673104image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:44.487190image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:48.817643image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:52.937192image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:57.483681image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:39:02.251218image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:37:47.088565image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:37:51.093932image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:37:54.947149image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:37:58.573433image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:01.845222image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:05.792367image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:09.833930image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:13.908214image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:17.875074image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:20.943447image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:24.327981image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:28.026796image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:32.329603image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:36.471178image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:40.878942image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:44.684338image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:49.051360image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:53.350718image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:57.715284image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:39:02.464112image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:37:47.235295image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:37:51.304317image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:37:55.117728image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:37:58.753215image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:01.988159image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:05.998528image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:09.979792image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:14.114073image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:18.017167image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:21.104602image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:24.513798image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:28.224635image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:32.539169image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:36.671695image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:41.095052image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:44.873222image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:49.274076image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:53.557417image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:57.926000image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:39:02.687503image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:37:47.393191image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:37:51.520152image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:37:55.295153image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:37:58.933897image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:02.141267image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:06.218493image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:10.155566image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:14.324273image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:18.164335image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:21.279653image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:24.667409image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:28.435957image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:32.756848image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:36.873754image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:41.320749image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:45.061808image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:49.492610image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:53.786142image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:58.127288image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:39:02.919237image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:37:47.562569image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:37:51.740788image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:37:55.451777image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:37:59.142414image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:02.296394image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:06.447649image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:10.333133image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:14.556279image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:18.332598image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:21.673378image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:24.838579image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:28.910635image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:32.976256image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:37.075614image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:41.538869image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:45.274617image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:49.719892image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:54.031673image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:58.362041image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:39:03.147243image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:37:47.726085image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:37:51.961477image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:37:55.600719image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:37:59.347613image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:02.453337image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:06.657514image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:10.545224image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:14.795859image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:18.497304image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:21.837454image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:25.011943image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:29.137652image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:33.182301image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:37.285321image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:41.770297image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:45.724118image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:49.931600image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:54.258742image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-16T13:38:58.579867image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Correlations

2025-01-16T13:39:18.675763image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Deviation of star ratingsFOG IndexFlesch Reading EaseRatingTopic_1Topic_2Topic_3Topic_4Topic_5avg_ratingbreadthdepthhelpfulnessnum_of_enrollednum_of_ratingsnum_of_reviewsnum_of_top_instructor_coursesnum_of_top_instructor_leanerssentiment_score_continuoussentiment_score_discretesst5_sentiment_scoretext_lengthtime_lapsed
Deviation of star ratings1.000-0.0200.0210.9390.172-0.006-0.0650.0340.096-0.790-0.0010.0260.164-0.453-0.510-0.483-0.315-0.406-0.3660.3710.375-0.026-0.017
FOG Index-0.0201.000-0.8290.056-0.0300.0460.1000.2190.0280.066-0.2040.1340.1450.0930.0890.079-0.033-0.0170.0330.0460.0570.6800.096
Flesch Reading Ease0.021-0.8291.0000.0220.027-0.053-0.143-0.249-0.080-0.0470.230-0.049-0.116-0.041-0.040-0.0310.0180.022-0.0900.0320.028-0.440-0.046
Rating0.9390.0560.0221.0000.0640.0430.0650.0450.0230.1170.0700.0620.0450.1130.1140.1190.0720.0930.4200.3860.3830.0740.033
Topic_10.172-0.0300.0270.0641.0000.022-0.3300.1590.249-0.126-0.0680.3690.025-0.099-0.094-0.088-0.059-0.090-0.3880.2180.126-0.065-0.028
Topic_2-0.0060.046-0.0530.0430.0221.000-0.2210.2560.3220.013-0.1180.4350.005-0.023-0.023-0.0220.0350.0130.0360.1270.0970.023-0.019
Topic_3-0.0650.100-0.1430.065-0.330-0.2211.000-0.037-0.0010.085-0.633-0.5360.0370.0840.0780.0750.0590.0700.0570.1250.0940.257-0.004
Topic_40.0340.219-0.2490.0450.1590.256-0.0371.0000.480-0.018-0.2190.3700.018-0.065-0.055-0.055-0.017-0.0410.0210.1190.108-0.034-0.009
Topic_50.0960.028-0.0800.0230.2490.322-0.0010.4801.000-0.062-0.2760.3690.017-0.077-0.074-0.067-0.008-0.041-0.1910.1110.096-0.052-0.035
avg_rating-0.7900.066-0.0470.117-0.1260.0130.085-0.018-0.0621.000-0.040-0.030-0.1150.4910.5720.5290.3860.4840.1830.0920.0950.0990.003
breadth-0.001-0.2040.2300.070-0.068-0.118-0.633-0.219-0.276-0.0401.0000.062-0.076-0.048-0.042-0.041-0.033-0.0310.0720.1370.108-0.2880.016
depth0.0260.134-0.0490.0620.3690.435-0.5360.3700.369-0.0300.0621.0000.019-0.012-0.0040.000-0.069-0.0660.0070.1300.1280.1170.078
helpfulness0.1640.145-0.1160.0450.0250.0050.0370.0180.017-0.115-0.0760.0191.000-0.123-0.142-0.140-0.072-0.098-0.1190.0260.0290.1690.020
num_of_enrolled-0.4530.093-0.0410.113-0.099-0.0230.084-0.065-0.0770.491-0.048-0.012-0.1231.0000.9370.9430.3530.6350.1570.0840.0920.1560.126
num_of_ratings-0.5100.089-0.0400.114-0.094-0.0230.078-0.055-0.0740.572-0.042-0.004-0.1420.9371.0000.9840.3840.6050.1520.0780.0910.1520.215
num_of_reviews-0.4830.079-0.0310.119-0.088-0.0220.075-0.055-0.0670.529-0.0410.000-0.1400.9430.9841.0000.3410.5690.1520.0850.0980.1410.201
num_of_top_instructor_courses-0.315-0.0330.0180.072-0.0590.0350.059-0.017-0.0080.386-0.033-0.069-0.0720.3530.3840.3411.0000.8510.0660.0670.049-0.029-0.324
num_of_top_instructor_leaners-0.406-0.0170.0220.093-0.0900.0130.070-0.041-0.0410.484-0.031-0.066-0.0980.6350.6050.5690.8511.0000.1140.0750.0780.004-0.186
sentiment_score_continuous-0.3660.033-0.0900.420-0.3880.0360.0570.021-0.1910.1830.0720.007-0.1190.1570.1520.1520.0660.1141.0000.7790.556-0.0700.028
sentiment_score_discrete0.3710.0460.0320.3860.2180.1270.1250.1190.1110.0920.1370.1300.0260.0840.0780.0850.0670.0750.7791.0000.4830.0640.037
sst5_sentiment_score0.3750.0570.0280.3830.1260.0970.0940.1080.0960.0950.1080.1280.0290.0920.0910.0980.0490.0780.5560.4831.0000.0750.038
text_length-0.0260.680-0.4400.074-0.0650.0230.257-0.034-0.0520.099-0.2880.1170.1690.1560.1520.141-0.0290.004-0.0700.0640.0751.0000.148
time_lapsed-0.0170.096-0.0460.033-0.028-0.019-0.004-0.009-0.0350.0030.0160.0780.0200.1260.2150.201-0.324-0.1860.0280.0370.0380.1481.000

Missing values

2025-01-16T13:39:03.457909image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-01-16T13:39:04.218807image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

course_nameRatingavg_ratingnum_of_ratingshelpfulnessReview_Textnum_of_reviewsnum_of_enrollednum_of_top_instructor_coursesnum_of_top_instructor_leanersdepthbreadthTopic_1Topic_2Topic_3Topic_4Topic_5sentiment_score_continuoussentiment_score_discretesst5_sentiment_scoretext_lengthtime_lapsedDeviation of star ratingsFOG IndexFlesch Reading Ease
0foundations-of-cybersecurity54.82790554The course is well paced and they get you comfortable with the topics even though we do not have any sort of prior exposure in this field It is very good for the beginners who are new to this field551098822332511153139-32.3540670.8370815.460677e-013.930000e-190.4444868.961206e-034.852320e-044.11144404900.219.0064.72
1foundations-of-cybersecurity54.82790544Information was well organized, easy to learn, and study with frequent note writing, and some breaks You can learn a good brief summary of whats to come, and what to research more in the future551098822332511153139-64.0238111.0711796.207771e-014.990000e-190.3792234.990000e-194.990000e-193.87944355590.217.4361.33
2foundations-of-cybersecurity54.82790541For a foundation course, this one was easy to understand, it explained all basic concepts in a fluid way and built up the base for the upcoming courses Im eager to move on to the other courses now551098822332511153139-81.7687860.6994715.870000e-195.870000e-191.0000005.870000e-195.870000e-194.25844385650.217.3149.83
3foundations-of-cybersecurity54.82790532I think this is a great start for anyone who is starting from absolute zero I think that since Ive been toying with the idea of getting into Cybersecurity for 2 years now, it was a great refresher551098822332511153139-48.5127362.4059871.943115e-039.377472e-010.0603103.300000e-193.300000e-194.64254383260.218.3649.83
4foundations-of-cybersecurity54.82790524Surprised by the quality of this course repeating items so you learn by seeing definitions and concepts over and over again while using great analogy to make difficult concept understandable551098822332511153139-63.5574871.0492612.940000e-196.030008e-010.3969992.940000e-192.940000e-194.50854305610.220.0041.03
5foundations-of-cybersecurity54.82790521Cybersecurity is a critical and multifaceted field that involves protecting computer systems, networks, and data from various digital threats Here is a review of the foundations of cybersecurity551098822332511153139-82.5611790.6994713.640000e-183.640000e-181.0000003.640000e-183.640000e-183.96144283990.222.630.76
6foundations-of-cybersecurity14.82790518In the certificate , why am i not getting my name printed Instead Coursera Learner551098822332511153139-82.5281910.6994713.370000e-183.370000e-181.0000003.370000e-183.370000e-182.08513155323.88.4665.73
7foundations-of-cybersecurity54.8279059Dear Instructors of the Foundations of Cybersecurity Course at Google,I wanted to take a moment to express my deepest gratitude for the incredible learning experience you provided throughout the course Your expertise, dedication, and passion for cybersecurity have truly made a lasting impact on my journey in this fieldFirstly, allow me to introduce myself My name is Your Name, and I embarked on this course with a strong desire to deepen my understanding of cybersecurity and acquire the necessary skills to contribute meaningfully to the industry As a lifelong learner, I am always seeking opportunities to expand my knowledge and stay updated with the latest developments in technology This course seemed like the perfect fit to enhance my cybersecurity expertiseI chose to take this course because I firmly believe that cybersecurity is a critical aspect of our increasingly digital world With the growing threats and vulnerabilities that organizations and individuals face, I wanted to equip myself with the knowledge and skills to make a tangible difference in securing digital systems and protecting sensitive information The Foundations of Cybersecurity course seemed like the ideal starting point to build a strong foundation in this fieldI am pleased to share that the course has exceeded my expectations in every way From the very beginning, the course structure, content, and delivery were impeccable The way you organized the modules and topics ensured a smooth learning journey, allowing me to grasp the fundamental concepts before diving into more advanced areasWhat I truly loved about the course was the emphasis on practicality The handson labs and simulations were invaluable in reinforcing the theoretical knowledge and providing a realworld perspective Being able to apply the concepts in a practical setting not only enhanced my technical skills but also instilled confidence in my ability to tackle realworld cybersecurity challengesFurthermore, the breadth of topics covered in the course was remarkable From threat analysis to network security, encryption, incident response, and compliance, every aspect was explored in depth, providing a comprehensive understanding of the cybersecurity landscape I appreciated the balance between theory and practical applications, as it allowed me to develop a holistic understanding of the subject matterYour dedication as instructors was evident throughout the course The quality of the course materials, including the informative videos, interactive quizzes, and additional readings, showcased the meticulous effort put into curating the content Your ability to simplify complex concepts and communicate them effectively is commendableThe course has had a significant impact on my professional growth Not only have I gained a solid understanding of cybersecurity fundamentals, but I have also acquired practical skills that I can immediately apply in realworld scenarios The course has expanded my career prospects and opened doors to exciting opportunities in the cybersecurity fieldI am truly grateful for the knowledge and insights I gained from this course Your guidance and expertise have played a crucial role in shaping my cybersecurity journey The impact you have made on my professional development is immeasurableThank you once again for your dedication, passion, and commitment to providing an exceptional learning experience I am proud to have been a student in the Foundations of Cybersecurity course at Google, and I look forward to continuing my cybersecurity journey with the skills and knowledge I have acquiredWith utmost appreciation,Jalal Saleem551098822332511153139-15.8751800.4575225.238462e-033.088402e-020.9486428.217229e-037.018596e-034.790545445390.2224.36-506.07
8foundations-of-cybersecurity34.8279059The questions on the quizzes were often meaningless and the multiple choice answers were unbelievably vague and illdefined to the point where no answer was entirely correct551098822332511153139-67.6008210.6209471.456140e-027.570000e-180.9854397.570000e-187.570000e-181.71122275601.816.7344.07
9foundations-of-cybersecurity54.8279058The instructors for the course did an amazing job at presenting all of the information to us The course is informative and definitely will expand your knowledge specifically over Cybersecurity551098822332511153139-49.9130680.6349336.520330e-049.816652e-030.9895313.730000e-193.730000e-194.77055305350.221.3324.11
course_nameRatingavg_ratingnum_of_ratingshelpfulnessReview_Textnum_of_reviewsnum_of_enrollednum_of_top_instructor_coursesnum_of_top_instructor_leanersdepthbreadthTopic_1Topic_2Topic_3Topic_4Topic_5sentiment_score_continuoussentiment_score_discretesst5_sentiment_scoretext_lengthtime_lapsedDeviation of star ratingsFOG IndexFlesch Reading Ease
144165java-programming-arrays-lists-data14.731680Not good515160710181071441-81.0271632.8406571.000000e+001.060000e-191.060000e-191.060000e-191.060000e-191.46513215363.70.80120.21
144166introduction-to-applied-cryptography54.6660This course by Professor Keith Martin is a fastpaced introduction to the basics of cryptography and six important classes of applicationsEven though the course is aimed at beginners, it contains a lot of material eg, on mobile telephony that will be of interest to advanced students as wellWhat I appreciated most about the course was the broad perspective and the relaxed manner in which serious knowledge was taught99693283454-14.7298200.3567591.836691e-022.151584e-029.150897e-013.176309e-021.326444e-023.97244684520.433.67-6.01
144167introduction-to-applied-cryptography54.6660Thank you so much during this 4 weeks, i gained more knowledges about cryptography in this course This course really help me to learn in flexiblw time99693283454-81.4843990.6994713.050000e-193.050000e-191.000000e+003.050000e-193.050000e-194.82554275640.413.7669.45
144168introduction-to-applied-cryptography54.6660very useful and informative course99693283454-81.5673670.6994713.690000e-193.690000e-191.000000e+003.690000e-193.690000e-194.6155555640.410.0049.48
144169introduction-to-applied-cryptography54.6660Challenging and very enratainent99693283454-82.5790360.6994713.790000e-183.790000e-181.000000e+003.790000e-183.790000e-184.4905444540.411.6050.50
144170introduction-to-applied-cryptography54.6660very good education99693283454-81.4960612.8406571.000000e+003.130000e-193.130000e-193.130000e-193.130000e-194.5145533890.414.5334.59
144171introduction-to-applied-cryptography54.6660good to learn99693283454-63.3543542.0409428.773162e-011.560000e-191.226838e-011.560000e-191.560000e-193.952443180.41.20119.19
144172introduction-to-applied-cryptography54.6660It is Good99693283454-81.0271632.8406571.000000e+001.060000e-191.060000e-191.060000e-191.060000e-193.9004433900.41.20119.19
144173introduction-to-applied-cryptography54.6660excellent99693283454-80.8987663.9860597.920000e-207.920000e-207.920000e-201.000000e+007.920000e-204.8335411970.440.40-47.99
144174introduction-to-applied-cryptography54.6660OK99693283454-69.0478830.5681563.040489e-025.740000e-179.695951e-015.740000e-175.740000e-173.4473414280.40.40121.22

Duplicate rows

Most frequently occurring

course_nameRatingavg_ratingnum_of_ratingshelpfulnessReview_Textnum_of_reviewsnum_of_enrollednum_of_top_instructor_coursesnum_of_top_instructor_leanersdepthbreadthTopic_1Topic_2Topic_3Topic_4Topic_5sentiment_score_continuoussentiment_score_discretesst5_sentiment_scoretext_lengthtime_lapsedDeviation of star ratingsFOG IndexFlesch Reading Ease# duplicates
0introcss54.895680Excellent course1630221414604384601-12.6606372.5630430.0009110.0077870.1871660.8008760.003264.84454215600.220.835.612